Proceedings of the 2nd International Workshop on Environmental Multimedia Retrieval 2015
DOI: 10.1145/2764873.2764875
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Automated Analysis of Wild Fish Behavior in a Natural Habitat

Abstract: This paper proposes a novel approach for the analysis of movement and behavior of the Plainfin midshipman (Porichthys notatus) in the wild. It is based on underwater video recordings of the fish in their natural habitat taken inside their nests during reproductive months. During this time, alpha male Plainfin midshipmen rarely leave their nests as they are guarding their eggs, so the proposed approach addresses the issue of detecting subtle motion and nesting behavior as the fish remains relatively sedentary. … Show more

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Cited by 8 publications
(4 citation statements)
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References 26 publications
(41 reference statements)
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“…For example, Wang et al described how "[a]eration events can be very subtle (e.g. when the fish sways slowly from side to side)" [80]. • only use subtle to describe how people acted outside of an interaction.…”
Section: Dataset Of Relevant Publicationsmentioning
confidence: 99%
“…For example, Wang et al described how "[a]eration events can be very subtle (e.g. when the fish sways slowly from side to side)" [80]. • only use subtle to describe how people acted outside of an interaction.…”
Section: Dataset Of Relevant Publicationsmentioning
confidence: 99%
“…A few studies have made use of some automation to analyze natural data (Wang et al 2016;Gabriel et al 2016), but none had focused on prediction of future events. Our work takes advantage of recent advances in computer vision to annotate a variety of natural data including automated movement estimation (Wang, Cullis-Suzuki, and Albu 2015;Poppe 2007) and pose recognition (Toshev and Szegedy 2014;Pfister, Charles, and Zisserman 2015).…”
Section: Related Workmentioning
confidence: 99%
“…Both of these fields have seen tremendous growth in recent years with increasing processor power and advances in methodology (Huang et al, 2014 ; Jordan and Mitchell, 2015 ). Computer vision techniques have been developed for a variety of tasks including automated movement estimation (Poppe, 2007 ; Wang et al, 2015 ), pose recognition (Toshev and Szegedy, 2014 ), object recognition (Erhan et al, 2014 ; Girshick et al, 2014 ), and activity classification (Ryoo and Matthies, 2013 ; Karpathy et al, 2014 ). In some cases, computer vision techniques have matched or surpassed single-human performance in recognizing arbitrary objects (He et al, 2015 ).…”
Section: Background and Related Workmentioning
confidence: 99%
“…Both of these fields have seen tremendous growth in recent years with increasing processor power and advances in methodology. 42,43 Computer vision techniques have been developed for a variety of tasks including automated movement estimation, 44,45 pose recognition, 46 object recognition, 47,48 and activity classification. 49,50 In some cases, computer vision techniques have matched or surpassed single-human performance in recognizing arbitrary objects.…”
Section: Background and Related Workmentioning
confidence: 99%